IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v20y2020i10p1645-1661.html
   My bibliography  Save this article

Forward-looking portfolio selection with multivariate non-Gaussian models

Author

Listed:
  • Michele Leonardo Bianchi
  • Gian Luca Tassinari

Abstract

In this study, we suggest a portfolio selection framework based on time series of stock log-returns, option-implied information, and multivariate non-Gaussian processes. We empirically assess a multivariate extension of the normal tempered stable (NTS) model and of the generalized hyperbolic (GH) one by implementing an estimation method that simultaneously calibrates the multivariate time series of log-returns and, for each margin, the univariate observed one-month implied volatility smile. To extract option-implied information, the connection between the historical measure P and the risk-neutral measure Q, needed to price options, is provided by the multivariate Esscher transform. The method is applied to fit a 50-dimensional series of stock returns, to evaluate widely known portfolio risk measures and to perform a forward-looking portfolio selection analysis. The proposed models are able to produce asymmetries, heavy tails, both linear and non-linear dependence and, to calibrate them, there is no need for liquid multivariate derivative quotes.

Suggested Citation

  • Michele Leonardo Bianchi & Gian Luca Tassinari, 2020. "Forward-looking portfolio selection with multivariate non-Gaussian models," Quantitative Finance, Taylor & Francis Journals, vol. 20(10), pages 1645-1661, October.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:10:p:1645-1661
    DOI: 10.1080/14697688.2020.1733057
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2020.1733057
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697688.2020.1733057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michele Leonardo Bianchi & Giovanni De Luca & Giorgia Rivieccio, 2020. "CoVaR with volatility clustering, heavy tails and non-linear dependence," Papers 2009.10764, arXiv.org.
    2. Michele Leonardo Bianchi, 2023. "Assessing and forecasting the market risk of bank securities holdings: a data-driven approach," Risk Management, Palgrave Macmillan, vol. 25(4), pages 1-23, December.
    3. Young Shin Kim, 2022. "Portfolio optimization and marginal contribution to risk on multivariate normal tempered stable model," Annals of Operations Research, Springer, vol. 312(2), pages 853-881, May.
    4. Kurosaki, Tetsuo & Kim, Young Shin, 2022. "Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk," Finance Research Letters, Elsevier, vol. 45(C).
    5. Massimo Arnone & Michele Leonardo Bianchi & Anna Grazia Quaranta & Gian Luca Tassinari, 2021. "Catastrophic risks and the pricing of catastrophe equity put options," Computational Management Science, Springer, vol. 18(2), pages 213-237, June.
    6. Tetsuo Kurosaki & Young Shin Kim, 2020. "Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk," Papers 2010.08900, arXiv.org.
    7. Roman N. Makarov, 2023. "Option Pricing and Portfolio Optimization under a Multi-Asset Jump-Diffusion Model with Systemic Risk," Risks, MDPI, vol. 11(12), pages 1-24, December.
    8. Michele Leonardo Bianchi & Asmerilda Hitaj & Gian Luca Tassinari, 2020. "Multivariate non-Gaussian models for financial applications," Papers 2005.06390, arXiv.org.
    9. Bianchi, Michele Leonardo & De Luca, Giovanni & Rivieccio, Giorgia, 2023. "Non-Gaussian models for CoVaR estimation," International Journal of Forecasting, Elsevier, vol. 39(1), pages 391-404.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:quantf:v:20:y:2020:i:10:p:1645-1661. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.